MilliSonic: Pushing the Limits of Acoustic Motion Tracking

Recent years have seen interest in device tracking and localization using acoustic signals. State-of-the-art acoustic motion tracking systems however do not achieve millimeter accuracy and require large separation between microphones and speakers, and as a result, do not meet the requirements for many VR/AR applications. Further, tracking multiple concurrent acoustic transmissions from VR devices today requires sacrificing accuracy or frame rate. We present MilliSonic, a novel system that pushes the limits of acoustic based motion tracking. Our core contribution is a novel localization algorithm that can provably achieve sub-millimeter 1D tracking accuracy in the presence of multipath, while using only a single beacon with a small 4-microphone array.Further, MilliSonic enables concurrent tracking of up to four smartphones without reducing frame rate or accuracy. Our evaluation shows that MilliSonic achieves 0.7mm median 1D accuracy and a 2.6mm median 3D accuracy for smartphones, which is 5x more accurate than state-of-the-art systems. MilliSonic enables two previously infeasible interaction applications: a) 3D tracking of VR headsets using the smartphone as a beacon and b) fine-grained 3D tracking for the Google Cardboard VR system using a small microphone array

Early Detection and Continuous Monitoring of Atrial Fibrillation from ECG Signals with a Novel Beat-Wise Severity Ranking Approach

Irregularities in heartbeats and cardiac functioning outside of clinical settings are often not available to the clinicians, and thus ignored. But monitoring these with high-risk population might assist in early detection and continuous monitoring of Atrial Fibrillation(AF). Wearable devices like smart watches and wristbands, which can collect Electrocardigraph(ECG) signals, can monitor and warn users of unusual signs in a timely manner. Thus, there is a need to develop a real-time monitoring system for AF from ECG. We propose an algorithm for a simple beat-by-beat ECG signal multilevel classifier for AF detection and a quantitative severity scale (between 0 to 1) for user feedback. For this study, we used ECG recordings from MIT BIH Atrial Fibrillation, MIT BIH Long-term Atrial Fibrillation Database. All ECG signals are preprocessed for reducing noise using filter. Preprocessed signal is analyzed for extracting 39 features including 20 of amplitude type and 19 of interval type. The feature space for all ECG recordings is considered for Classification. Training and testing data include all classes of data i.e., beats to identify various episodes for severity. Feature space from the test data is fed to the classifier which determines the class label based on trained model. A class label is determined based on number of occurences of AF and other arrhythmia episodes such as AB(Atrial Bigeminy), SBR(Sinus Bradycardia), SVTA(Supra Ventricular Tacchyarrhythmia). Accuracy of 96.7764% is attained with Random Forest algorithm, Furthermore, precision and recall are determined based on correct and incorrect classifications for each class. Precision and recall on average of Random Forest Classifier are obtained as 0.968 and 0.968 respectievely. This work provides a novel approach to enhance existing method of AF detection by identifying heartbeat class and calculates a quantitative severity metric that might help in early detection and continuous monitoring of AF

Commercial-scale CCS Project in Decatur, Illinois – Construction Status and Operational Plans for Demonstration

AbstractUnited States Department of Energy (DOE) and Archer Daniels Midland Company (ADM) has made substantial progress in the development and construction of the largest saline storage project in the U.S. This commercial-scale project is located at the ADM's agricultural processing and biofuels complex in Decatur, Illinois. The Office of Fossil Energy's National Energy Technology Laboratory manages this project. Detailed design, installation of the CO2 compression, dehydration, and transmission system, and installation of related piping, electrical, and instrumentation was completed and commissioning of this system was initiated. The construction of a 100-MW electrical substation, which will supply power to the compressors and other equipment, is in progress. A 2206 m deep monitoring well and a 1083 m geophysical well were drilled. The U.S. Environmental Protection Agency (EPA) issued a draft permit for a Class VI injection well with a capacity to inject 3000 tonnes of CO2 per day. This is expected to be the first geological sequestration project to operate with EPA's Class VI well permit in the U.S. The project is scheduled to begin CO2 injection into the Mount Simon Sandstone, a deep saline reservoir, early 2015. The project team members include Schlumberger Carbon Services, Illinois State Geological Survey (ISGS)-University of Illinois, and Richland Community College (RCC). Public education and outreach for CCS is an integral part of this ICCS project and to this end, the project has established the National Sequestration Education Center (NSEC) at RCC in Decatur. NSEC is implementing a new associate degree program, first in the United States, with an emphasis on CCS. This project was recognized by the Carbon Sequestration Leadership Forum

Order Statistics Based Diversity Combining for Fading Channels

In this paper we present a new order statistics based diversity combining scheme (OSDC) for combining a set of independently fading signal amplitudes. The OSDC orders all the received signal amplitudes and uses only the two strongest signals in the combining process. The decision as to whether to use only the strongest or both the strongest and the next strongest is made depending on the relative strengths of these two highest order statistics. Signal-to-noise ratio performance of the new scheme is compared with that of the traditional schemes such as, selection combining, maximal ratio combining, equal gain combining, and a second order selection combining (SC2), for three channels, namely Rayleigh, Nakagami and exponential. The results show that OSDC performs as well as SC2

ZigZag Decoding: Combating Hidden Terminals in Wireless Networks

This paper presents ZigZag, an 802.11 receiver that combats hidden terminals. ZigZag exploits 802.11 retransmissions which, in the case of hidden terminals, cause successive collisions. Due to asynchrony, these collisions have different interference-free stretches at their start, which ZigZag uses to bootstrap its decoding. ZigZag makes no changes to the 802.11 MAC and introduces no overhead when there are no collisions. But, when senders collide, ZigZag attains the same throughput as if the colliding packets were a priori scheduled in separate time slots. We build a prototype of ZigZag in GNU Radio. In a testbed of 14 USRP nodes, ZigZag reduces the average packet loss rate at hidden terminals from 82.3% to about 0.7%

iJam: Jamming Oneself for Secure Wireless Communication

Wireless is inherently less secure than wired networks because of its broadcast nature. Attacks that simply snoop on the wireless medium successfully defeat the security of even 802.11 networks using the most recent security standards (WPA2-PSK). In this paper we ask the following question: Can we prevent this kind of eavesdropping from happening? If so, we can potentially defeat the entire class of attacks that rely on snooping. This paper presents iJam, a PHY-layer protocol for OFDM-based wireless systems. iJam ensures that an eavesdropper cannot successfully demodulate a wireless signal not intended for it. To achieve this iJam strategically introduces interference that prevents an eavesdropper from decoding the data, while allowing the intended receiver to decode it. iJam exploits the properties of 802.11â s OFDM signals to ensure that an eavesdropper cannot even tell which parts of the signal are jammed. We implement iJam and evaluate it in a testbed of GNURadios with an 802.11-like physical layer. We show that iJam makes the data bits at the adversary look random, i.e., the BER becomes close to 50%, whereas the receiver can perfectly decode the data